In this paper, we report data and experiments related to the research article entitled “An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization” by Caliciotti et. Al. . In particular, in , large scale unconstrained optimization problems are considered by applying linesearch-based truncated Newton methods. In this framework, a key point is the reduction of the number of inner iterations needed, at each outer iteration, to approximately solving the Newton equation. A novel adaptive truncation criterion is introduced in  to this aim. Here, we report the details concerning numerical experiences over a commonly used test set, namely CUTEst . Moreover, comparisons are reported in terms of performance profiles , adopting different parameters settings. Finally, our linesearch-based scheme is compared with a renowned trust region method, namely TRON .
2018, DATA IN BRIEF, Pages 246-255 (volume: 17)
Data and performance profiles applying an adaptive truncation criterion, within linesearch-based truncated Newton methods in large scale nonconvex optimization (01a Articolo in rivista)
Caliciotti Andrea, Fasano Giovanni, Nash S. G., Roma Massimo
Gruppo di ricerca: Continuous Optimization